A global navigation satellite system (GNSS) is a system for estimating positions of moving objects moving all over the earth using a radio wave emitted from a satellite orbiting a space orbit and is widely used for a military purpose such as missile guidance, for tracking a position of a smartphone user, and for a navigation system of a vehicle, a ship, an aircraft, and the like nowadays. Representative examples of the GNSS include a global positioning system (GPS) of the United States, a GLONASS of Russia, Galileo of Europe, a quasi-zenith satellite system (QZSS) of Japan, and the like. However, the GNSS cannot perform a localization in an indoor space where a radio wave emitted from a satellite cannot reach and has a problem that localization accuracy is significantly decreased in the center of a city due to blocking, reflection, and the like of the radio wave by skyscrapers.
In recent years, automobile manufacturers around the world, and global corporations such as Google and Intel have fostered research and development of an autonomous vehicle. However, partial autonomous driving in an outdoor space makes some results, but autonomous driving in an indoor space and an outdoor space is still impossible due to inability of an indoor localization of the GNSS. In order to solve the problem of the GNSS, a wireless localization technique for estimating a position of a user or a vehicle using a radio signal existing in an indoor space draws much attention. The wireless localization technology is currently being commercialized and serviced, but localization accuracy is very low compared with the GNSS, and thus, various types of wireless localization technology are under development.
Wireless communication can be classified into short-range wireless communication and wide-area wireless communication. A representative example of the short-range wireless communication includes Wi-Fi, Bluetooth, Zigbee, and the like, and a representative example of the wide-area wireless communication includes 3rd generation (3G), 4th generation (4G), Lora, and the like. Long term evolution (LTE) is a kind of 4G wireless communication. The short-range wireless communication such as Bluetooth and ZigBee is not suitable for a localization because of characteristics that temporarily occur in an indoor space according to needs of a user and disappear. Currently, a Wi-Fi signal and an LTE signal are known to be distributed in most indoor spaces.
Accordingly, a WiFi position system (WPS) that performs a localization using a Wi-Fi signal of a band of 2.4 GHz is in the spotlight. A representative localization technique which uses the WiFi signal may include a triangulation technique and a fingerprint technique. The triangulation technique estimates a position by measuring a received signal strength (RSS) from three or more access points (APs) and converting the received signal strength into a distance. However, since attenuation, reflection, diffraction, or the like of a radio signal occurs due to a wall of a building, an obstacle, people, and the like in an indoor space, the converted distance value includes a large error, and thereby, the triangulation technique is rarely used for an indoor localization.
For this reason, the fingerprint technique is mainly used in the indoor space. This technique divides the indoor space into a grid structure, collects values of signal strength in each unit area, and builds a radio map by storing the values in a database. In a state where the radio map is built as described above, a position of a user is estimated by comparing strength of the signal received at the position of the user with data of the radio map. Since the technique collects data in which spatial characteristics of the indoor space is reflected, the technique has an advantage that localization accuracy is higher than the triangulation technique. As wireless environment is good and many signals are collected by finely dividing the indoor space, the localization precision may be increased up to 2 to 3 meters.
The fingerprint technique performs relatively accurate localization in a case where there is little difference between strength of a signal collected at the time of building a radio map and strength of a signal collected at the time of localization. However, a change in the wireless environment, such as a signal interference between communication channels frequently occurring in the real world, expansion of an access point, occurrence of failure or an obstacle, and the like leads to collection of signal strength different from data of a radio map built in the past, which results in a serious impact on localization accuracy. Accordingly, various attempts have been made to increase the localization accuracy by applying a k-nearest neighbor (KNN), a particle filter or the like to the fingerprint technique.
First of all, due to the fact that a Wi-Fi signal is distributed actually only in a part of the center of a city due to characteristic of short-range wireless communication, the fingerprint technique has an inherent limitation that cannot be used alone for a vehicle navigation system requiring a localization service in both an indoor space and an outdoor space, or autonomous driving. The LTE signal is uniformly distributed in the indoor space and the outdoor space, but there is a limitation to increase a localization accuracy because an area where a change in the signal strength is not large is wide. As a result, the localization service which uses the LTE signal remains at a level in which an approximate position of a user is provided, and there are still many problems to be used for a vehicle navigation system or autonomous driving in which a localization error can lead to an accident.